{"id":"W3107332205","doi":"10.2196/25573","title":"Author’s Response to Peer Review of “Predicting Health Disparities in Regions at Risk of Severe Illness to Inform Health Care Resource Allocation During Pandemics: Observational Study”","year":2020,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Observational study; Pandemic; Health care; Peer review; Resource allocation; Coronavirus disease 2019 (COVID-19); Resource (disambiguation); Medicine; Computer science; Political science; Disease; Economic growth; Economics; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003495141,0.0001282297,0.000615344,0.0002520449,0.0001835175,0.000009594876,0.000209734,0.0000483456,0.000009555045],"category_scores_gemma":[0.003493181,0.0001511135,0.0000639115,0.0007686333,0.00001581439,0.0001036877,0.0001937152,0.0001733663,0.00001244041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000669426,"about_ca_system_score_gemma":0.0001882106,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007696059,"about_ca_topic_score_gemma":0.003563864,"domain_scores_codex":[0.9975181,0.0001959725,0.001455107,0.0003241666,0.000195433,0.000311268],"domain_scores_gemma":[0.9983343,0.0001562368,0.000737032,0.0003274644,0.0001713681,0.0002735541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00134518,0.0002058123,0.6443352,0.02212919,0.0001078654,0.00000248555,0.2217847,0.002703927,0.000003322729,0.01741214,0.08542185,0.004548356],"study_design_scores_gemma":[0.000622951,0.0003901675,0.6272594,0.00133918,0.000004362535,5.817107e-7,0.007319799,0.0001042354,0.000004606118,0.00009345222,0.3626816,0.0001795935],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5048478,0.002206938,0.0001496918,0.4903929,0.00006433621,0.001694411,0.0004270852,0.00002514875,0.0001916263],"genre_scores_gemma":[0.9825021,0.0007612218,0.0003921482,0.01436164,0.00005591557,0.000232585,0.00007583048,0.0000191424,0.001599405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4776543,"threshold_uncertainty_score":0.9989118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1754999771730678,"score_gpt":0.3681972299514891,"score_spread":0.1926972527784213,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}